Sami Romdhani

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Generative 3D face models are a powerful tool in computer vision. They provide pose and illumination invariance by modeling the space of 3D faces and the imaging process. The power of these models comes at the cost of an expensive and tedious construction process, which has led the community to focus on more easily constructed but less powerful models. With(More)
We show how to extend the ICP framework to nonrigid registration, while retaining the convergence properties of the original algorithm. The resulting optimal step nonrigid ICP framework allows the use of different regularisations, as long as they have an adjustable stiffness parameter. The registration loops over a series of decreasing stiffness weights,(More)
This paper presents a novel algorithm aiming at analysis and identification of faces viewed from different poses and illumination conditions. Face analysis from a single image is performed by recovering the shape and textures parameters of a 3D Morphable Model in an analysis-by-synthesis fashion. The shape parameters are computed from a shape error(More)
Recovering the shape of any 3D object using multiple 2D views requires establishing correspondence between feature points at different views. However changes in viewpoint introduce self-occlusions, resulting nonlinear variations in the shape and inconsistent 2D features between views. Here we introduce a multi-view nonlinear shape model utilising 2D(More)
We present a novel algorithm aiming to estimate the 3D shape, the texture of a human face, along with the 3D pose and the light direction from a single photograph by recovering the parameters of a 3D morphable model. Generally, the algorithms tackling the problem of 3D shape estimation from image data use only the pixels intensity as input to drive the(More)
This paper describes an algorithm for finding faces within an image. The basis of the algorithm is to run an observation window at all possible positions, scales and orientation within the image. A non-linear support vector machine is used to determine whether or not a face is contained within the observation window. The non-linear support vector machine(More)
We present a novel approach for recognizing faces in images taken from different directions and under different illumination. The method is based on a 3D morphable face model that encodes shape and texture in terms of model parameters, and an algorithm that recovers these parameters from a single image of a face. For face identification, we use the shape(More)
By Sami Romdhani1, Philip Torr2, Bernhard Sch ö lkopf3 and Andrew Blake4 1University of Basel, Bernoullistrasse 16, 4056 Basel, Switzerland ( 2Oxford Brookes University, Department of Computing, Oxford OX33 1HX, UK ( 3Max-Planck-Institutes for Biological Cybernetics, Spemannstraße 38, 72076 Tübingen, Germany(More)
This paper presents a novel statistical shape model that can be used to detect and localise feature points of a class of objects in images. The shape model is inspired from the 3D morphable model (3DMM) and has the property to be viewpoint invariant. This shape model is used to estimate the probability of the position of a feature point given the position(More)